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As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

We discuss the effects of image scanning microscopy using doughnut beam illumination on the properties of signal strength and integrated intensity. Doughnut beam illumination can give better optical sectioning and background rejection than Airy disk illumi ...

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

The opportunistic pathogen Pseudomonas aeruginosa adapts to solid surfaces to enhance virulence and infect its host. Type IV pili (T4P), long and thin filaments that power surface-specific twitching motility, allow single cells to sense surfaces and contro ...

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological ...

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model upd ...

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherica ...

Guided Mesh Normal Filtering

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Extensions of Peer Prediction Incentive Mechanisms

Robust machine learning for neuroscientific inference

Reliable data-driven decision-making through optimal transport

Aggregating Spatial and Photometric Context for Photometric Stereo

Graph generative deep learning models with an application to circuit topologies

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Robust discrete choice models with t-distributed kernel errors

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Regularization for distributionally robust state estimation and prediction

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Image scanning microscopy with a doughnut beam: signal strength and integrated intensity

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Two antagonistic response regulators control Pseudomonas aeruginosa polarization during mechanotaxis

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee

Price Discrimination with Robust Beliefs

Federated Linear Bandit Learning via Over-the-air Computation

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI